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RESEARCH ARTICLE
Predicting the effects of eutrophication mitigation on predatoryfish biomass and the value of recreational fisheries
Goran Sundblad , Lena Bergstrom , Tore Soderqvist ,
Ulf Bergstrom
Received: 14 June 2019 / Revised: 11 September 2019 / Accepted: 14 September 2019 / Published online: 9 October 2019
Abstract Improving water clarity is a core objective for
eutrophication management in the Baltic Sea, but may
influence fisheries via effects on fish habitat suitability. We
apply an ensemble of species distribution models coupled
with habitat productivity functions and willingness-to-pay
estimates to assess these effects for two coastal predatory
fish species, European perch (Perca fluviatilis) and
pikeperch (Sander lucioperca). The models predicted a
37% increase in perch and 59% decrease in pikeperch
biomass if reaching the reference level for water clarity in
the Baltic Sea Action Plan. Reaching the target level was
predicted to increase perch biomass by 13%. However, the
associated economic gain for the recreational fisheries
sector was countervailed by an 18% pikeperch reduction.
Still, a net benefit was predicted since there are six times
more fishing days for perch than pikeperch. We exemplify
how ecological modelling can be combined with economic
analyses to map and evaluate management alternatives.
Keywords Economic value � Ecosystem services �Eutrophication � Fisheries � Species distribution model �Travel cost method
INTRODUCTION
Increasing pressure from human activities requires envi-
ronmental strategies and policies that can ensure a sus-
tained delivery of ecosystem services (Costanza et al. 1997;
Halpern et al. 2015). Supporting science-based evaluations
of policy development and implementation, coupled sce-
nario analyses provide potentially useful tools for inte-
grating ecology, social science and policymaking (Coreau
et al. 2009). By evaluating the effects of changes in envi-
ronmental forcing (including human-induced pressures) in
spatially explicit, process-based ecological models, the
development of ecosystem functions and services can be
explored for a set of plausible futures, i.e. scenarios (Qiu
et al. 2018). Extending the analysis to include expected net
changes in the economic value of benefits provisioned by
the ecosystem under different scenarios opens the possi-
bility to explore economic benefits and trade-offs (Stal
et al. 2008; Costanza et al. 2014; Bauer et al. 2018).
As a key approach to improve environmental manage-
ment of the Baltic Sea, the Baltic Marine Environment
Protection Commission (HELCOM) has adopted the Baltic
Sea Action Plan (BSAP; HELCOM 2007; Backer et al.
2010), to be updated by 2021. The plan is dedicated to
turning scientific knowledge into strategic policy imple-
mentation, focusing on four themes; eutrophication, bio-
diversity, hazardous substances and maritime activities.
Among these, eutrophication is a major problem in the
Baltic Sea as a consequence of long-lasting inputs of
nitrogen and phosphorus since the mid-1900s, which to a
large extent remain in the basin due to its semi-enclosed
nature (Andersen et al. 2015). A core indicator for fol-
lowing up on the status of eutrophication is water clarity,
which represents the water’s permeability to light, mea-
sured as the Secchi depth during summer (HELCOM
2018a, b). Water clarity is a suitable indicator of eutroph-
ication since it shows a strong relationship with chlorophyll
a and the abundance of pelagic primary producers in the
water column, which benefit from elevated nutrient levels
at sea and are indicative of eutrophication (Fleming-Le-
htinen and Laamanen 2012). Threshold values for the water
clarity indicator are sub-basin specific, and are set based on
scientific evaluation and common agreement among
countries around the Baltic Sea (HELCOM 2018a, b). The
BSAP sets reference levels for water clarity based on
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https://doi.org/10.1007/s13280-019-01263-1
historical data, and target levels to 25% deviation from the
reference level (HELCOM 2007; Backer et al. 2010).
Nutrient loading to the Baltic Sea is currently decreasing
in response to reduction measures (HELCOM 2018a).
Eutrophication at sea is also being relieved in some
aspects, even though several challenges remain due to
pollution legacies as well as biotic and abiotic processes in
the ecosystem (Andersen et al. 2015; HELCOM 2018a).
The elevated nutrient conditions increase the production of
ephemeral primary producers, such as phytoplankton and
filamentous algae, which leads to shading and impaired
growth conditions for larger, habitat-forming vegetation,
such as bladderwrack and perennial macrophytes (Berger
et al. 2004; Austin et al. 2017), and subsequent deteriora-
tion of associated ecosystem services (Ronnback et al.
2007). For example, many fish species rely on the habitats
shaped by structurally complex vegetation during early life
stages, and therefore degradation or loss of vegetated
habitats is associated with negative effects on population
abundances (Mumby et al. 2004; Seitz et al. 2013; Hansen
et al. 2018).
However, responses to changes in eutrophication can be
species specific. The two percid fishes, Eurasian perch
(Perca fluviatilis; hereafter perch) and pikeperch (Sander
lucioperca) are both important species for commercial and
recreational fishing in the Baltic Sea (Lehtonen et al. 1996;
Adjers et al. 2006). These large predatory fishes may
through their predation, leading to a trophic cascade
decreasing the growth of filamentous algae, indirectly
relieve the ecosystem symptoms of eutrophication (Ostman
et al. 2016; Donadi et al. 2017). Both species spawn in
spring and juveniles spend their first summer in shallow,
sheltered and warm inlets and bays (Lehtonen et al. 1996;
Snickars et al. 2010). One important difference between
them is their adaptation to water clarity. Perch prefers clear
water and pikeperch more turbid environments, such as
those created under elevated nutrient conditions (Sand-
strom and Karas 2002; Ljunggren and Sandstrom 2007;
Veneranta et al. 2011), suggesting contrasting population
level effects of reducing symptoms of eutrophication
(Bergstrom et al. 2013).
Overall, eutrophication mitigation appears to provide
economic net benefits. A recent cost-benefit analysis of a
cost-effective international nutrient abatement programme
meeting BSAP objectives indicated an annual net gain
amounting to about € 2300 million (Scharin et al. 2016).
Regarding recreational activities in the Baltic Sea, esti-
mates based on travel cost approaches for its nine border-
ing states suggest that the total annual recreation benefits
are close to € 15 billion, but could increase by 7–18%
under a water quality improvement scenario (Czajkowski
et al. 2015). Transdisciplinary models also suggest net
benefits of nutrient load reductions for the commercial
fisheries in Baltic Sea offshore areas, and the use of spatial
models highlight geographical differences (Bauer et al.
2018). However, the impact on the recreational fishery and
the associated economic value is less known.
Since reducing eutrophication is a slow and inmany cases
complex process, it is necessary to examine potential con-
sequences of mitigation measures on species, functions and
ecosystem services to support effective and relevant mea-
sures. Furthermore, as ecosystem responses to eutrophica-
tion vary geographically, it is important to take spatial
variability into account (Bergstrom et al. 2013; Bauer et al.
2018). Species distribution models provide a tool which can
predict potential effects of changes in eutrophication in
higher geographical detail. In these models, the distribution
of species is related to a set of explanatory variables
describing the biophysical environment of the species, such
as depth, wave exposure and Secchi depth. These environ-
mental variables are then used to predict the distribution of a
species on a map (Elith and Leathwick 2007). The use of
species distribution models in research and management has
developed quickly during the last decade and a variety of
fundamentally different modelling methods are available,
each with their strength and weaknesses (e.g. Bucas et al.
2013). To utilize the strengths of conceptually different
modelling techniques, several methods can be combined in
an ensemble approach (Araujo and New 2007).
Here, we evaluate quantitative scenarios for changes in
water clarity to assess the chain of events from eutrophica-
tionmitigation to potential effects on coastal fish distribution
and biomass, and the associated economic value for recre-
ational fisheries. We achieve this by applying species dis-
tribution models in an ensemble approach, evaluating
different scenarios, which we combine with habitat pro-
ductivity functions (Sundblad et al. 2014) and an economic
assessment on willingness-to-pay (Soderqvist et al. 2005).
The analyses are concentrated around the BSAP core indi-
cator water clarity, which is a key predictor of suitable re-
cruitment habitats for perch and pikeperch (Bergstrom et al.
2013). In addition, by economically valuing the changes in
biomass from a recreational fisheries perspective, we aim at
indicating the economic net impact of this particular effect of
combatting eutrophication. In so doing we illustrate how
cross-disciplinary approaches can contribute with scientific
knowledge in support of management in line with a sus-
tainable development.
MATERIALS AND METHODS
Scenarios
The eutrophication scenarios are rooted in the Baltic Sea
Action Plan environmental objective ‘‘a Baltic Sea
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unaffected by eutrophication’’ (HELCOM 2007; Backer
et al. 2010), represented by different values for the core
indicator on water clarity (HELCOM 2018a). Seven sce-
narios for effects on species distributions were applied as in
Bergstrom et al. (2013), designed as follows: (1) the situ-
ation by the onset of the Baltic Sea Action Plan in 2007
(0% change, hereafter termed initial conditions); (2) a
slightly deteriorating condition (10% decrease in Secchi
depth) and (3) five scenarios representing improved water
clarity (11, 20, 30, 40 and 48% increase in Secchi depth).
Levels 11 and 48% correspond to the target and reference
levels for the Baltic Proper stated in the original Baltic Sea
Action plan (HELCOM 2007; Backer et al. 2010; Berg-
strom et al. 2013). The target levels have later been revised
(HELCOM 2018b), but involved only minor changes
compared to Backer et al. (2010; Table 1).
Ecological modelling
The species distribution models identified potential
recruitment habitats for perch and pikeperch based on field
surveys of spawning and young-of-the-year fish (Berg-
strom et al. 2013). The species-environment relationships,
including water clarity as estimated by the Secchi depth,
were modelled using three different statistical modelling
techniques: GAM, Maxent and random forest (Wood 2006;
Cutler et al. 2007; Phillips and Dudık 2008). The predicted
recruitment habitats for the two species under the different
scenarios for water clarity and using three modelling
techniques rendered in total 42 recruitment habitat maps
(25 m cell resolution, Bergstrom et al. 2013). Compared to
Bergstrom et al. (2013), this study was limited to Swedish
waters (Counties of Sodermanland, Stockholm and Upp-
sala, see Fig. 2) in order to overlap with the economic data
(below).
Habitat productivity functions
Habitat productivity functions, which describe the rela-
tionship between the amount of recruitment habitat and the
density of large fish (Sundblad et al. 2014), were combined
with the 42 recruitment habitat maps (Bergstrom et al.
2013), to predict the biomass of large perch ([ 20 cm) and
pikeperch ([ 30 cm) under the different scenarios. This
was done in two steps. First, for every cell in each of the 42
maps we estimated habitat availability as a proportion (the
area predicted to function as recruitment habitat divided by
the total water area) within typical maximum migration
distances. Typical migration distances were defined as the
distances within which 80% of perch and 75% of pikeperch
are recaptured based on tagging studies; 10 km for perch
and 15 km for pikeperch (Saulamo and Neuman 2002;
Sundblad et al. 2014). Thereby, each cell in the resulting
raster expressed the amount of habitat within the migration
distance of that cell. The calculations were made using
focal statistics in ArcGIS, Esri. Unlike Sundblad et al.
(2014), migration barriers, i.e. islands and land, could not
be taken into account, since computational constraints
restricted the use of cost distance functions from each cell
(instead of from each population as applied in Sundblad
et al. 2014). As a consequence, the focal search window
potentially over- or under-estimated habitat availability,
depending on the amount of barriers and the amount of
habitat that then could, or could not, be reached. This was
apparent at the very local scale, while it should be of less
importance for the study area as a whole.
Secondly, based on the estimated habitat availability
under different scenarios, habitat productivity functions as
presented in Sundblad et al. (2014) were used to calculate
the expected catch-per-unit-effort (CPUE, number of fish
per net and night) of adult perch and pikeperch ([ 20 and
[ 30 cm, respectively). The habitat productivity functions
were CPUE = 2.03 (± 0.69 SE) * ln(x) ? 9.39 (± 1.27 SE)
for perch (n = 12, p = 0.015, r2= 0.46), and CPUE = 0.05
(± 0.02 SE) * ln(x ? 0.02) ? 0.21 (± 0.04 SE) for pike-
perch (n = 12, p = 0.013, r2= 0.48), where x was the
habitat availability calculated in the first step. The analyses
resulted in 42 new maps, in which each cell showed the
expected CPUE of large predatory fish hypothetically
applying a standardized gill net test fishing in that cell.
Lastly, the average and associated uncertainty (1 standard
error, SE), of the three modelling techniques was calcu-
lated for each eutrophication level scenario.
The predicted CPUE of large perch and pikeperch were
evaluated against observed catches in standardized gill nets
based on data from 11 existing monitoring sites in the study
area (locations of sites are shown in Fig. 2). Three of these
sites had also been utilized in the development of the
habitat productivity functions (Sundblad et al. 2014), but
here included more years (2002–2016) compared to the
previous study (2005–2006). For the evaluation of pike-
perch, one site was excluded as it was no longer repre-
sentative to the conditions under which the habitat
productivity functions were developed, due to implemen-
tation of a total fishing ban in 2010–2015 (Bergstrom et al.
2016). Values for CPUE were converted to biomasses
(kg ha-1) using existing conversion functions (Heibo and
Karas 2005), and information on the average weight of a
perch[ 20 cm and a pikeperch[ 30 cm, respectively, for
the gear type used (information from the national fish
monitoring database KUL). Based on data on abundances
and weights per length group, the average weight of a
perch ([ 20 cm) was estimated to 0.23 kg (n = 10 941),
and 0.51 kg for pikeperch ([ 30 cm, n = 355). Since
coastal perch is primarily found at 0–10 m depths,
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observed CPUE in monitoring and all maps have been
limited to these depths for perch (Fig. 2).
Economic analyses
The effect on opportunities for recreational fishing of perch
and pikeperch was monetized using data from Soderqvist
et al. (2005), who estimated the recreational value of
fishing in the study area in 2002 through applications of the
travel cost method (TCM). The TCM is a revealed pref-
erence method that makes use of travel data obtained
through surveys for estimating people’s demand for visit-
ing various recreational sites (Freeman et al. 2014). As the
expected catches of a fish species varies across different
sites, data on travels, associated travel costs and the vari-
ation in catches can allow an estimation of people’s eco-
nomic trade-offs between travel costs and catches,
expressed by people’s willingness-to-pay (WTP) for an
increased catch. In the study by Soderqvist et al. (2005),
randomly selected members of the Swedish Anglers
Association (n = 2000) and the general public (n = 2000)
living in the counties of Uppsala and Stockholm replied to
mail questionnaires relating to their use of the archipelago,
including information about visits to sites in the study area,
their fishing at those sites, the distance travelled, travel
time, travel costs and catches for different fish species
(expressed as weight-per-unit-effort, WPUE). These data
allowed the quantification of explanatory variables in
conditional logit models predicting the probability that a
particular fishing site is selected, and coefficients from the
estimated models were subsequently used for computing
the WTP for a changed WPUE (see Soderqvist et al. 2005
for details). Finally, the results on individual WTP was
related to another survey, conducted annually since 2013
by Statistics Sweden, focusing on the recreational fishing
habits among the Swedish general public (Swedish Agency
for Marine and Water Management, SwAM 2019). From
the national survey we calculated the relative species
preference as the ratio of the fishing effort for perch to the
fishing effort for pikeperch. Specifically, using yearly
averages (2013–2017) and associated uncertainty (95% CI)
from the Baltic proper, effort was defined as the sum of
fishing occasions per year (using any gear) from respon-
dents who reported having caught perch
(505 000 ± 124 000 ‘gear days’) and pikeperch,
(83 000 ± 64 000) respectively.
RESULTS
The CPUE of large perch and pikeperch predicted by the
habitat productivity functions had a strong fit to the CPUE
observed at monitoring sites (Fig. 1). Average predicted
CPUE was 5.4 (SD = 2.3) for perch and 0.16 (SD = 0.10)
for pikeperch, while observed CPUE was 6.2 for perch (SD
= 3.9) and 0.08 for pikeperch (SD = 0.08). Linear regres-
sion models (y = ax ? b, where y = observed CPUE and x =
predicted CPUE) resulted in, for perch: a = 1.1 ± 0.4 (SE),
b = 0.06 ± 2.5 (SE; n = 11) and explained 44% of the
variation in observed CPUE (p = 0.027, F(1, 9) = 7.0), and
for pikeperch: a = 0.54 ± 0.21 (SE), b = - 0.007 ± 0.04
(SE, n = 10) and explained 45% of the variation in
observed CPUE (p = 0.035, F(1, 8) = 6.4).
The resulting fish biomass maps show that pikeperch is
mainly concentrated to large bays close to the mainland,
corresponding to areas typically characterized by higher
eutrophication levels (lower Secchi depth). Perch biomass
was more evenly distributed across the archipelago, how-
ever with a dominance in the middle parts (Fig. 2). Cal-
culated for the entire study area, average predicted biomass
of perch[ 20 cm under the initial scenario (0% change)
was 9.4 kg per hectare (± 0.33 SD) and of pikeperch
[ 30 cm it was 0.65 kg per hectare (± 0.48 SD, Fig. 2).
An increase in Secchi depth, indicating a reduction in
the level of eutrophication, was predicted to increase the
Fig. 1 Regression relationships for spatial predictions of CPUE
(number per net and night) and observed CPUE in standardized
monitoring for perch and pikeperch. Note that points (monitoring
areas) can overlap, as indicated by darker grey)
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biomass of perch and reduce the biomass of pikeperch
(Fig. 3).
The results from the TCM study (Soderqvist et al. 2005)
indicated a mean WPUE in recreational fishing of 0.8 kg
per fishing hour for perch (SD 1.5) and 0.3 kg per fishing
hour for pikeperch (SD 0.6) during the spring of 2002. For
both species there was a statistically significant positive
relationship between WPUE on the probability that a par-
ticular fishing site was selected (Soderqvist et al. 2005).
The mean WTP for one additional kg of fish per fishing
hour was estimated in 2002 prices at SEK 71.6 for perch
and SEK 153 for pikeperch, which corresponds to EUR
8.29 and EUR 18.0, respectively, in 2018 prices after
adjusting for inflation and applying an exchange rate of
EUR 1 = SEK 10.2567. Hence, pikeperch showed to be
more highly valued than perch. However, as the WPUE for
pikeperch was lower, a 1 kg increase corresponds to a
substantially higher relative increase (1.3/0.3 = 333%)
compared to for perch (1.8/0.8 = 125%). In comparison to
these estimates, more moderate changes are suggested by
the scenario analyses (Fig. 4). For example, an improve-
ment of water clarity to the level of the BSAP target (11%
increase) was suggested to lead to a 13% increase of perch
biomass (Fig. 4). Assuming linearity between the predicted
biomass and WPUE in the recreational fisheries, as well as
between WPUE and WTP, this would correspond to an
increase of 0.8 * 13% = 0.10 kg perch per fishing hour, and
the WTP for this increase corresponds to 0.10 * 8.29 =
EUR 0.8 for perch. Following the same assumptions the
WPUE of pikeperch would decrease with 18% as a result
of increased water clarity (Fig. 4), giving a decrease of 0.3
* 18% = 0.05 kg per fishing hour. This corresponds to an
economic loss of 0.05 * 18.0 = EUR 0.9. The results show
that for the recreational value of fishing of these species,
the increase in water transparency entails an economic gain
in perch that is countervailed by a loss in pikeperch.
However, as perch is 6.1 (± 5.0, 95% confidence interval)
times more often targeted by recreational fishers (SwAM
2019), a net economic benefit of eutrophication mitigation
can be expected with respect to these two fish species.
DISCUSSION
Our analyses outline how an improved water clarity, in
accordance with political commitment to improve the
eutrophication status of the Baltic Sea, can affect the dis-
tribution of coastal fish recruitment habitats, and how this
may change the prevalence of large predatory fish and the
extent of recreational fisheries in the coastal zone. By
combining species distribution modelling with habitat
productivity functions, scenarios for changes in the focal
Fig. 2 Estimated biomass distribution (kg ha-1) of perch[ 20 cm (left) and pikeperch[ 30 cm (right). Biomass estimates were based on spatial
predictions of recruitment habitats (Bergstrom et al. 2013) combined with habitat productivity functions (Sundblad et al. 2014) under a set of
eutrophication scenarios. The maps show average scenarios from three different modelling techniques under initial Secchi depth conditions (0%
change) and a 48% increase, which corresponds to the reference level for water clarity in the Baltic Sea Action Plan. Stars denote the location of
standardized gillnet monitoring areas used for validation of the biomass predictions (Fig. 1). Note that the maximum biomass per hectare for each
species differs between scenarios (legends), and that areas deeper than 10 m depth have been excluded for perch. The inset shows the location of
the study area within the Baltic Sea
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fish populations were mapped, enabling a spatially explicit
evaluation. By extending the assessment to impacts on the
economic value for the recreational fisheries sector, we
illustrate how ecological scenarios for environmental
management can be integrated with analyses of economic
impacts in a quantitative analysis.
The analyses serve a dual purpose, considering ecolog-
ical as well as economic aspects of eutrophication miti-
gation, focusing on potential impacts on the abundance of
large predatory fish and on recreational fisheries, respec-
tively. In addition to the effects on economic value asso-
ciated with recreational fisheries, effects on other types of
ecosystem services can also be anticipated. Large predatory
fish represent a key ecosystem component in the coastal
zone, with a strong contribution to ecosystem function as
well as to provisioning and regulating ecosystem services
(Holmlund and Hammer 1999; de Groot et al. 2002). In
their environment, large predatory fish regulate the pres-
ence of other species via predation. Prey species typically
regulated by large predatory fish are medium sized meso-
predatory fish, which are observed to expand in population
sizes under conditions of reduced predator availability
(Ritchie and Johnson 2009; Bergstrom et al. 2019). One
recent example from the study area shows a connection
between decreasing predator abundance and dominance of
sticklebacks in coastal areas, causing unexpected and
detrimental effects on the food web (Donadi et al. 2017).
As a result of trophic cascades, the regulation of prey by
predatory fish species indirectly affects primary producers,
counteracting excessive occurrences of ephemeral fila-
mentous algae (Eriksson et al. 2009; Donadi et al. 2017).
Thereby, the predatory fish may contribute to relieving
eutrophication symptoms. Meta-analyses indicate that
changes in this regulation from large predatory fish can be
as important as changes in nutrient loadings for controlling
the presence of nuisance algae (Ostman et al. 2016). In
relation to human well-being, predatory fish also contribute
through the provisioning of food and cultural values
enabled by commercial and recreational fisheries. The
current study focused on the recreational fishing sector,
which is dominating over commercial fishing in the studied
coastal areas (SwAM 2019).
Although ecological feedback loops can be expected to
increase the quantitative uncertainty of the presented pre-
dictions, the overall conclusions of our study, showing
increasing perch and decreasing pikeperch under reduced
eutrophication, appear likely also in relation to a mecha-
nistic understanding of the fish species physiological
responses to water clarity (Sandstrom and Karas 2002;
Ljunggren and Sandstrom 2007; Veneranta et al. 2011), as
Fig. 4 Relative change (%) in coastal predatory fish biomass within
the studied coastal area, at different eutrophication levels. Uncertainty
(grey areas, 1 standard error) was calculated across three different
modelling techniques (see text)
Fig. 3 Average (per hectare; left axis) and total (for whole study
area; right axis) predicted biomass of perch ([ 20 cm) and pikeperch
([ 30 cm) at different eutrophication levels within the studied
archipelago area. Uncertainty (grey areas, 1 standard error) was
calculated across three different modelling techniques (see text)
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well as with changes observed in fish populations in the
Baltic Sea during the decades when eutrophication
increased (Bergstrom et al. 2013). As such, results from
scenario analyses are useful for providing examples of
likely management outcomes. Here, the predictive models,
based on empirically assessed quantitative relationships,
provide a basis for economic analyses in support of an
integrated strategic evaluation.
A key factor enabling this study was the combination of
a number of quantitative analyses based on unique empir-
ical data covering various temporal and spatial scales as
well as different disciplines. However, such an extended
scope may also mean that more sources of uncertainty are
introduced. For instance, the value of the recreational
fisheries stems from surveys performed in the Stockholm
archipelago in 2002 (Soderqvist et al. 2005), while the
national fishing habits and species preferences originates
from surveys performed 2013–2017, on a larger scale (the
Baltic Proper, SwAM 2019). Another important potential
source of uncertainty is the fish biomass map predictions.
These were evaluated by (i) comparing predicted CPUE
with results for specific monitoring sites across the study
area and (ii) by comparing the resulting biomass estimates
for the entire study area with independent biomass
assessments. The evaluation of predictions against moni-
toring data showed an overall good predictive ability,
although the predicted CPUE of pikeperch was biased
towards higher than observed values (Fig. 1). This over-
estimation influences the absolute values obtained (Figs. 2,
3), but are expected to have smaller impact on the relative
changes in biomass (Fig. 4) and on the economic assess-
ment, which rely on relative changes and WTP for change
in WPUE in the recreational fisheries (Soderqvist et al.
2005). Regarding the biomass estimates, few studies were
available for comparison. In two well-studied coastal sites
in the northern part of the study area (Forsmark and
Kallrigafjarden), perch biomass has previously been esti-
mated at 34 and 30 kg ha-1 and pikeperch biomass at 3.7
and 6.6 kg ha-1 (Heibo and Karas 2005), for fish above
approximately 10 cm. In the central Baltic, south of the
study area, biomass of perch[ 10 cm in the summer has
been estimated at 38 kg ha-1 in an enclosed coastal site
(Adill and Andersson 2006). Including also smaller fish,
the same biomass density has been found in a comparison
of 100 fish populations in 38 lakes (Downing and Plante
1993). A direct comparison is difficult as the cited esti-
mates are related to particular sites primarily consisting of
suitable habitats, while our study encompasses also large
parts of the outer archipelago, where recruitment habitats
are scarce and predicted biomasses were very low (Fig. 2),
thus lowering averages for the entire study area over which
the population is distributed. Additionally, different length
classes of fish have been included. In order to provide more
direct comparisons, we utilized length frequency distribu-
tions from monitoring in the study area and calculated the
proportion of fish 10–20 and[ 20 cm for perch, and 10–30
and[ 30 cm for pikeperch. Based on these proportions the
average (across the entire study area) predicted biomass of
fish[ 10 cm was 16 kg ha-1 for perch and 1.5 kg ha-1 of
pikeperch. Additionally, a direct comparison was possible
for a subset of the study area, which overlapped with one of
the previously published results (Forsmark and Kallri-
gafjarden). In this subset, length correction of predicted
biomasses yielded for perch 50 and 45 kg ha-1 for each
subarea respectively, and for pikeperch 1.2 and 4.2 kg ha-1
respectively, which is more similar to Heibo and Karas
(2005).
The evaluation of management scenarios identifies
potential trade-offs when several ecosystem and economic
aspects are included. Here, a gain of the perch recreational
fisheries was predicted to be countervailed by a loss in the
pikeperch fisheries, when WTP for a changed WPUE was
considered individually for the two species. However,
when scaling the results to the volume of recreational
fishing on both species, a total net gain was predicted, since
the total number of fishing days targeting perch is much
higher than for pikeperch. The results imply an economic
benefit from mitigating eutrophication from a recreational
fisheries perspective. A full economic valuation should also
consider other species of relevance, as well as impacts on
other ecosystem services, and take into account the fact
that use values such as recreational values are only one
type of economic value associated with a particular
ecosystem service; non-use values such as existence values
should typically be added (Freeman et al. 2014). As one
example, Northern pike (Esox lucius) is highly targeted by
the recreational fisheries, but could not be included here
since habitat productivity functions are lacking for this
species. Still, knowledge of the ecology of pike suggests
that a similar response to eutrophication mitigation as for
perch could be expected (Sandstrom et al. 2005; Engstrom-
Ost and Mattila 2008; Salonen et al. 2009), implying that
the net benefit for recreational fisheries could be larger than
the estimates presented. Including additional ecosystem
services associated to predatory fish, such as biological
regulation (Donadi et al. 2017), would likely also con-
tribute to increase the estimated benefits of eutrophication
mitigation, supporting previous results which show net
benefits as the most common overall outcome (Czajkowski
et al. 2015; Scharin et al. 2016; Bauer et al. 2018).
In conclusion, our analyses demonstrate the usefulness
of integrating economic information with quantitative
ecological predictions in a spatial context. Combining the
spatial approach with scenario analyses is beneficial as it
allows mapping potential trade-offs and net outcomes of
123� The Author(s) 2019
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1096 Ambio 2020, 49:1090–1099
management alternatives at both more detailed and large-
scale levels.
Acknowledgements Open access funding provided by Swedish
University of Agricultural Sciences. We thank Gustav Blomqvist at
the Swedish Agency for Marine and Water Management for providing
national recreational fisheries statistics. This study was in part funded
by the Swedish Environmental Protection Agency through the project
VALUES (13/132) and ECOCOA (17/89).
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
made.
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AUTHOR BIOGRAPHIES
Goran Sundblad (&) is a Researcher at the Department of Aquatic
Resources at the Swedish University of Agricultural Sciences (SLU).
He holds a Ph.D. in Aquatic Ecology from Uppsala University. His
work is related to habitat, food web ecology and aquatic resource use
in coastal and freshwater systems.
Address: Department of Aquatic Resources, Institute of Freshwater
Research, Swedish University of Agricultural Sciences (SLU), Stan-
gholmsvagen 2, 178 93 Drottningholm, Sweden.
e-mail: [email protected]
Lena Bergstrom is an associate professor at the Department of
Aquatic Resources at SLU. She holds a Ph.D. in Ecology from Umea
University. Her research concerns marine ecosystems and food webs
with a focus on environmental assessment and management.
Address: Department of Aquatic Resources, Institute of Coastal
Research, Swedish University of Agricultural Sciences (SLU), Skol-
gatan 6, 742 42 Oregrund, Sweden.
e-mail: [email protected]
123� The Author(s) 2019
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1098 Ambio 2020, 49:1090–1099
Tore Soderqvist is an Affiliated Researcher at Anthesis Enveco and
Associate Professor of Economics at the Stockholm School of Eco-
nomics. His research includes economic valuation of ecosystem ser-
vices, cost-benefit analysis and sustainability assessments.
Address: Anthesis Enveco, Barnhusgatan 4, 111 23 Stockholm,
Sweden.
e-mail: [email protected]
Ulf Bergstrom is a Researcher at the Department of Aquatic
Resources at SLU. He holds a Ph.D. in Marine Ecology from Umea
University. His research concerns fish and food web ecology, and
management of coastal ecosystems.
Address: Department of Aquatic Resources, Institute of Coastal
Research, Swedish University of Agricultural Sciences (SLU), Skol-
gatan 6, 742 42 Oregrund, Sweden.
e-mail: [email protected]
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Ambio 2020, 49:1090–1099 1099